Released Snowflake ARA-R01 Updated Questions PDF [Q55-Q77]

Released Snowflake ARA-R01 Updated Questions PDF

ARA-R01 Dumps and Practice Test (135 Exam Questions)

NEW QUESTION # 55
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?

• A. Call the LOGIN_HISTORY Information Schema table function.
• B. View the Users section in the Account tab in the Snowflake UI and review the last login column.
• C. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text
  "LOGIN".
• D. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.

Answer: D

Explanation:
This view can be used to query login attempts by Snowflake users within the last 365 days (1 year). It provides information such as the event timestamp, the user name, the client IP, the authentication method, the success or failure status, and the error code or message if the login attempt was unsuccessful. By querying this view, the IT Security team can identify any suspicious or malicious login attempts to Snowflake and take appropriate actions to prevent credential stuffing attacks1. The other options are not the best ways to find recent and ongoing login attempts to Snowflake. Option A is incorrect because the LOGIN_HISTORY Information Schema table function only returns login events within the last 7 days, which may not be sufficient to detect credential stuffing attacks that span a longer period of time2. Option C is incorrect because the History tab in the Snowflake UI only shows the queries executed by the current user or role, not the login events of other users or roles3. Option D is incorrect because the Users section in the Account tab in the Snowflake UI only shows the last login time for each user, not the details of the login attempts or the failures.

NEW QUESTION # 56
An Architect on a new project has been asked to design an architecture that meets Snowflake security, compliance, and governance requirements as follows:
1) Use Tri-Secret Secure in Snowflake
2) Share some information stored in a view with another Snowflake customer
3) Hide portions of sensitive information from some columns
4) Use zero-copy cloning to refresh the non-production environment from the production environment To meet these requirements, which design elements must be implemented? (Choose three.)

• A. Define row access policies.
• B. Use Dynamic Data Masking.
• C. Use the Business-Critical edition of Snowflake.
• D. Create a secure view.
• E. Use the Enterprise edition of Snowflake.
• F. Create a materialized view.

Answer: B,C,D

Explanation:
These three design elements are required to meet the security, compliance, and governance requirements for the project.
To use Tri-Secret Secure in Snowflake, the Business Critical edition of Snowflake is required. This edition provides enhanced data protection features, such as customer-managed encryption keys, that are not available in lower editions. Tri-Secret Secure is a feature that combines a Snowflake-maintained key and a customer-managed key to create a composite master key to encrypt the data in Snowflake1.
To share some information stored in a view with another Snowflake customer, a secure view is recommended. A secure view is a view that hides the underlying data and the view definition from unauthorized users. Only the owner of the view and the users who are granted the owner's role can see the view definition and the data in the base tables of the view2. A secure view can be shared with another Snowflake account using a data share3.
To hide portions of sensitive information from some columns, Dynamic Data Masking can be used.
Dynamic Data Masking is a feature that allows applying masking policies to columns to selectively mask plain-text data at query time. Depending on the masking policy conditions and the user's role, the data can be fully or partially masked, or shown as plain-text4.

NEW QUESTION # 57
Which of the below commands will use warehouse credits?

• A. SELECT COUNT(*) FROM SNOWFLAKE;
• B. SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;
• C. SHOW TABLES LIKE 'SNOWFL%';
• D. SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;

Answer: A,B,D

Explanation:
Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake.
A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.
Among the commands listed in the question, the following ones will use warehouse credits:
SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.
SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scanthe SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.
SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command
will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.
The command that will not use warehouse credits is:
SHOW TABLES LIKE 'SNOWFL%': This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern 'SNOWFL%' in the current database and schema5. Therefore, option A is incorrect.
References: : Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

NEW QUESTION # 58
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)

• A. Choose lower cardinality columns to support clustering keys and cost effectiveness.
• B. Choose cluster columns that are most actively used in selective filters.
• C. Choose TIMESTAMP columns with nanoseconds for the highest number of unique rows.
• D. Choose cluster columns that are actively used in the GROUP BY clauses.
• E. Choose columns that are frequently used in join predicates.

Answer: B,E

Explanation:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
References:
Clustering Keys & Clustered Tables | Snowflake Documentation
[Considerations for Choosing Clustering for a Table | Snowflake Documentation]

NEW QUESTION # 59
When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?

• A. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.
• B. All rows loaded using a specific COPY statement will have the same timestamp value.
• C. All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.
• D. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.

Answer: B

Explanation:
According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source.
References:
Snowflake Documentation: Loading Data into Tables with Default Values
Snowflake Documentation: COPY INTO table

NEW QUESTION # 60
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?

• A. Materialized view
• B. External table
• C. Search optimization
• D. Result cache

Answer: A

Explanation:
A materialized view is a feature that provides the capability to define an alternate cluster key for a table with an existing cluster key. A materialized view is a pre-computed result set that is stored in Snowflake and can be queried like a regular table. A materialized view can have a different cluster key than the base table, which can improve the performance and efficiency of queries on the materialized view. A materialized view can also support aggregations, joins, and filters on the base table data. A materialized view is automatically refreshed when the underlying data in the base table changes, as long as the AUTO_REFRESH parameter is set to true1.
References:
Materialized Views | Snowflake Documentation

NEW QUESTION # 61
Why might a Snowflake Architect use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake? (Select TWO).

• A. Snowflake cannot handle the joins implied in a 3NF data model.
• B. The Architect wants to present a simple flattened single view of the data to a particular group of end users.
• C. The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.
• D. The Architect is designing a landing zone to receive raw data into Snowflake.
• E. The Architect wants to remove data duplication from the data stored in Snowflake.

Answer: B,C

Explanation:
A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:
A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.
A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.
A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.
The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:
Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.
The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.
The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.
References:
Snowflake Architect Training
Data Modeling: Understanding the Star and Snowflake Schemas
Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
Star Schema vs Snowflake Schema: 5 Key Differences
Dimensional Data Modeling - Snowflake schema
Star schema vs Snowflake Schema

NEW QUESTION # 62
What is a valid object hierarchy when building a Snowflake environment?

• A. Organization --> Account --> Stage --> Table --> View
• B. Account --> Database --> Schema --> Warehouse
• C. Organization --> Account --> Database --> Schema --> Stage
• D. Account --> Schema > Table --> Stage

Answer: C

Explanation:
This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:
Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-account data sharing, billing and usage reporting, and single sign-on across accounts12.
Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.
Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption, and ownership56.
Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as ownership and default warehouse .
Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .
The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is incorrect.
References:
Snowflake Documentation: Organizations
Snowflake Blog: Introducing Organizations in Snowflake
Snowflake Documentation: Accounts
Snowflake Blog: Understanding Snowflake Account Structures
Snowflake Documentation: Databases
Snowflake Blog: How to Create a Database in Snowflake
[Snowflake Documentation: Schemas]
[Snowflake Blog: How to Create a Schema in Snowflake]
[Snowflake Documentation: Stages]
[Snowflake Blog: How to Use Stages in Snowflake]

NEW QUESTION # 63
The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?

• A. The warehouse MY_WH will only be active when there are results in the stream.
• B. The warehouse MY_WH will automatically resize to accommodate the size of the stream.
• C. The warehouse MY_WH will be made active every five minutes to check the stream.
• D. The warehouse MY_WH will never suspend.

Answer: A

Explanation:
The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:
[CREATE TASK | Snowflake Documentation]
[Using Streams and Tasks | Snowflake Documentation]
[CREATE WAREHOUSE | Snowflake Documentation]

NEW QUESTION # 64
There are two databases in an account, named fin_db and hr_db which contain payroll and employee data, respectively. Accountants and Analysts in the company require different permissions on the objects in these databases to perform their jobs. Accountants need read-write access to fin_db but only require read-only access to hr_db because the database is maintained by human resources personnel.
An Architect needs to create a read-only role for certain employees working in the human resources department.
Which permission sets must be granted to this role?

• A. USAGE on database hr_db, SELECT on all schemas in database hr_db, SELECT on all tables in database hr_db
• B. USAGE on database hr_db, USAGE on all schemas in database hr_db, REFERENCES on all tables in database hr_db
• C. MODIFY on database hr_db, USAGE on all schemas in database hr_db, USAGE on all tables in database hr_db
• D. USAGE on database hr_db, USAGE on all schemas in database hr_db, SELECT on all tables in database hr_db

Answer: D

Explanation:
To create a read-only role for certain employees working in the human resources department, the role needs to have the following permissions on the hr_db database:
USAGE on the database: This allows the role to access the database and see its schemas and objects.
USAGE on all schemas in the database: This allows the role to access the schemas and see their objects.
SELECT on all tables in the database: This allows the role to query the data in the tables.
Option A is the correct answer because it grants the minimum permissions required for a read-only role on the hr_db database.
Option B is incorrect because SELECT on schemas is not a valid permission. Schemas only support USAGE and CREATE permissions.
Option C is incorrect because MODIFY on the database is not a valid permission. Databases only support USAGE, CREATE, MONITOR, and OWNERSHIP permissions. Moreover, USAGE on tables is not sufficient for querying the data. Tables support SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, and OWNERSHIP permissions.
Option D is incorrect because REFERENCES on tables is not relevant for querying the data.
REFERENCES permission allows the role to create foreign key constraints on the tables.
References:
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#database-privileges
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#schema-privileges
: https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#table-privileges

NEW QUESTION # 65
Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)

• A. An external table can be created with a row access policy, and the policy can be applied to the VALUE column.
• B. A row access policy can be applied to the VALUE column of an existing external table.
• C. While cloning a database, both the row access policy and the external table will be cloned.
• D. External tables are supported as mapping tables in a row access policy.
• E. A row access policy cannot be directly added to a virtual column of an external table.
• F. A row access policy cannot be applied to a view created on top of an external table.

Answer: A,B,E

Explanation:
These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
References:
CREATE EXTERNAL TABLE | Snowflake Documentation
ALTER EXTERNAL TABLE | Snowflake Documentation
Understanding Row Access Policies | Snowflake Documentation
Snowflake Data Governance: Row Access Policy Overview
Secure Views | Snowflake Documentation

NEW QUESTION # 66
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

• A. The query has overly complex logic.
• B. The query is reading from remote storage.
• C. The query is processing a very large dataset.
• D. The query is queued for execution.

Answer: A

Explanation:
Compilation time is the time it takes for the optimizer to create an optimal query plan for the efficient execution of the query. It also involves some pruning of partition files, making the query execution efficient2 If the compilation time is greater than the execution time, it means that the optimizer spent more time analyzing the query than actually running it. This could indicate that the query has overly complex logic, such as multiple joins, subqueries, aggregations, or expressions. The complexity of the query could also affect the size and quality of the query plan, which could impact the performance of the query3 To reduce the compilation time, the Architect can try to simplify the query logic, use views or common table expressions (CTEs) to break down the query into smaller parts, or use hints to guide the optimizer. The Architect can also use the EXPLAIN command to examine the query plan and identify potential bottlenecks or inefficiencies4 References:
1: SnowPro Advanced: Architect | Study Guide 5
2: Snowflake Documentation | Query Profile Overview 6
3: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time 7
4: Snowflake Documentation | Optimizing Query Performance 8
: SnowPro Advanced: Architect | Study Guide
: Query Profile Overview
: Understanding Why Compilation Time in Snowflake Can Be Higher than Execution Time
: Optimizing Query Performance

NEW QUESTION # 67
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

• A. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
  '2021-05-01 10:00:00');
• B. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.
• C. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
• D. Undrop the STAGING schema and then rerun the CLONE statement.

Answer: C

Explanation:
The error message indicates that the schema STAGING does not have time travel data available for the requested timestamp, because the current version of the schema was created on 2021-06-02 23:00:00, which is after the timestamp of 2021-06-01 08:00:00. Therefore, the CLONE statement cannot access the historical data of the schema at that point in time.
Option A is incorrect, because undropping the STAGING schema will not restore the previous version of the schema that was active on 2021-06-01 08:00:00. Instead, it will create a new version of the schema with the same name and no data or objects.
Option B is incorrect, because modifying the timestamp to 2021-05-01 10:00:00 will not clone the schema as it looked one week ago, but as it looked when it was first created. This may not reflect the desired state of the schema and its objects.
Option C is correct, because renaming the STAGING schema and performing an UNDROP to retrieve the previous STAGING schema version will restore the schema that was dropped on 2021-06-02
23:00:00. This schema has time travel data available for the requested timestamp of 2021-06-01
08:00:00, and can be cloned using the CLONE statement.
Option D is incorrect, because cloning can be accomplished by using the UNDROP command to access the previous version of the schema that was active during the proposed time travel period.
References: : Cloning Considerations : Understanding & Using Time Travel : CREATE <object> ... CLONE

NEW QUESTION # 68
A healthcare company wants to share data with a medical institute. The institute is running a Standard edition of Snowflake; the healthcare company is running a Business Critical edition.
How can this data be shared?

• A. The healthcare company will need to change the institute's Snowflake edition in the accounts panel.
• B. Set the share_restriction parameter on the shared object to false.
• C. Contact Snowflake and they will execute the share request for the healthcare company.
• D. By default, sharing is supported from a Business Critical Snowflake edition to a Standard edition.

Answer: B

Explanation:
By default, Snowflake does not allow sharing data from a Business Critical edition to a non-Business Critical edition. This is because Business Critical edition provides enhanced security and data protection features that are not available in lower editions. However, this restriction can be overridden by setting the share_restriction parameter on the shared object (database, schema, or table) to false. This parameter allows the data provider to explicitly allow sharing data with lower edition accounts. Note that this parameter can only be set by the data provider, not the data consumer. Also, setting this parameter to false may reduce the level of security and data protection for the shared data.
References:
Enable Data Share:Business Critical Account to Lower Edition
Sharing Is Not Allowed From An Account on BUSINESS CRITICAL Edition to an Account On A Lower Edition SQL Execution Error: Sharing is Not Allowed from an Account on BUSINESS CRITICAL Edition to an Account on a Lower Edition Snowflake Editions | Snowflake Documentation

NEW QUESTION # 69
What are purposes for creating a storage integration? (Choose three.)

• A. Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.
• B. Manage credentials from multiple cloud providers in one single Snowflake object.
• C. Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.
• D. Support multiple external stages using one single Snowflake object.
• E. Avoid supplying credentials when creating a stage or when loading or unloading data.
• F. Control access to Snowflake data using a master encryption key that is maintained in the cloud provider's key management service.

Answer: A,D,E

Explanation:
A storage integration is a Snowflake object that stores a generated identity and access management (IAM) entity for an external cloud provider, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. This integration allows Snowflake to read data from and write data to an external storage location referenced in an external stage1.
One purpose of creating a storage integration is to support multiple external stages using one single Snowflake object. An integration can list buckets (and optional paths) that limit the locations users can specify when creating external stages that use the integration. Note that many external stage objects can reference different buckets and paths and use the same storage integration for authentication1.
Therefore, option C is correct.
Another purpose of creating a storage integration is to avoid supplying credentials when creating a stage or when loading or unloading data. Integrations are named, first-class Snowflake objects that avoid the need for passing explicit cloud provider credentials such as secret keys or access tokens. Integration objects store an IAM user ID, and an administrator in your organization grants the IAM user permissions in the cloud provider account1. Therefore, option D is correct.
A third purpose of creating a storage integration is to store a generated IAM entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account. For example, you can create a storage integration for Amazon S3 even if your Snowflake account is hosted on Azure or Google Cloud Platform. This allows you to access data across different cloud platforms using Snowflake1.
Therefore, option B is correct.
Option A is incorrect, because creating a storage integration does not control access to Snowflake data using a master encryption key. Snowflake encrypts all data using a hierarchical key model, and the master encryption key is managed by Snowflake or by the customer using a cloud provider's key management service. This is independent of the storage integration feature2.
Option E is incorrect, because creating a storage integration does not create private VPC endpoints.
Private VPC endpoints are a network configuration option that allow direct, secure connectivity between VPCs without traversing the public internet. This is also independent of the storage integration feature3.
Option F is incorrect, because creating a storage integration does not manage credentials from multiple cloud providers in one single Snowflake object. A storage integration is specific to one cloud provider, and you need to create separate integrations for each cloud provider you want to access4.
References: : Encryption and Decryption : Private Link for Snowflake : CREATE STORAGE INTEGRATION : Option 1: Configuring a Snowflake Storage Integration to Access Amazon S3

NEW QUESTION # 70
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?

• A. Use AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then use an external table against the Blob storage.
• B. Use a materialized view on top of an external table against the S3 bucket in AWS Singapore.
• C. Copy the data between providers from S3 to Azure Blob storage to collocate, then use Snowpipe for data ingestion.
• D. Use an external table against the S3 bucket in AWS Singapore and copy the data into transient tables.

Answer: D

Explanation:
Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges.
The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.
Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a table that is not subject to the Time Travel and Fail-safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.
Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.
Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. The external table will also access the external data files every time a query is executed, which may affect the query performance.
References: 1: Materialized Views 2: External Tables 3: Transient Tables 4: Snowpipe Overview 5: AWS Transfer Family

NEW QUESTION # 71
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

• A. Table
• B. Schema
• C. Global
• D. Database

Answer: C

Explanation:
The object type level at which the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges can be granted is global. These are account-level privileges that control who can apply or unset these policies on objects such as columns, tables, views, accounts, or users. These privileges are granted to the ACCOUNTADMIN role by default, and can be granted to other roles as needed.
The other options are incorrect because they are not the object type level at which these privileges can be granted. Database, schema, and table are lower-level object types that do not support these privileges. References: Access Control Privileges | Snowflake Documentation, Using Dynamic Data Masking | Snowflake Documentation, Using Row Access Policies | Snowflake Documentation, Using Session Policies | Snowflake Documentation

NEW QUESTION # 72
When using the copy into <table> command with the CSV file format, how does the match_by_column_name parameter behave?

• A. The parameter will be ignored.
• B. The command will return a warning stating that the file has unmatched columns.
• C. The command will return an error.
• D. It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name.

Answer: A

Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2.
Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as AmazonComprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets.
PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
References: 1: Snowpipe Overview 2: Using Streams and Tasks to Automate Data Pipelines 3: External Functions Overview 4: Snowflake Data Marketplace Overview : [Loading Data Using COPY INTO] : [What is Amazon EMR?] : [PySpark Overview] The copy into <table> command is used to load data from staged files into an existing table in Snowflake. The command supports various file formats, such as CSV, JSON, AVRO, ORC, PARQUET, and XML1.
The match_by_column_name parameter is a copy option that enables loading semi-structured data into separate columns in the target table that match corresponding columns represented in the source data. The parameter can have one of the following values2:
CASE_SENSITIVE: The column names in the source data must match the column names in the target table exactly, including the case. This is the default value.
CASE_INSENSITIVE: The column names in the source data must match the column names in the target table, but the case is ignored.
NONE: The column names in the source data are ignored, and the data is loaded based on the order of the columns in the target table.
The match_by_column_name parameter only applies to semi-structured data, such as JSON, AVRO, ORC, PARQUET, and XML. It does not apply to CSV data, which is considered structured data2.
When using the copy into <table> command with the CSV file format, the match_by_column_name parameter behaves as follows2:
It expects a header to be present in the CSV file, which is matched to a case-sensitive table column name. This means that the first row of the CSV file must contain the column names, and they must match the column names in the target table exactly, including the case. If the header is missing or does not match, the command will return an error.
The parameter will not be ignored, even if it is set to NONE. The command will still try to match the column names in the CSV file with the column names in the target table, and will return an error if they do not match.
The command will not return a warning stating that the file has unmatched columns. It will either load the data successfully if the column names match, or return an error if they do not match.
References:
1: COPY INTO <table> | Snowflake Documentation
2: MATCH_BY_COLUMN_NAME | Snowflake Documentation

NEW QUESTION # 73
Which Snowflake data modeling approach is designed for BI queries?

• A. 3 NF
• B. Star schema
• C. Data Vault
• D. Snowflake schema

Answer: B

Explanation:
A star schema is a Snowflake data modeling approach that is designed for BI queries. A star schema is a type of dimensional modeling that organizes data into fact tables and dimension tables. A fact table contains the measures or metrics of the business process, such as sales amount, order quantity, or profit margin. A dimension table contains the attributes or descriptors of the business process, such as product name, customer name, or order date. A star schema is called so because it resembles a star, with one fact table in the center and multiple dimension tables radiating from it. A star schema can improve the performance and simplicity of BI queries by reducing the number of joins, providing fast access to aggregated data, and enabling intuitive query syntax. A star schema can also support various types of analysis, such as trend analysis, slice and dice, drill down, and roll up12.
References:
Snowflake Documentation: Dimensional Modeling
Snowflake Documentation: Star Schema

NEW QUESTION # 74
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)

• A. Compute costs must be optimized.
• B. There needs to be fewer objects per tenant.
• C. Security and Role-Based Access Control (RBAC) policies must be simple to configure.
• D. Storage costs must be optimized.
• E. Tenant data shape may be unique per tenant.

Answer: D,E

Explanation:
An Account Per Tenant strategy means creating a separate Snowflake account for each tenant (customer or business unit) of the multi-tenant application.
This approach has some advantages and disadvantages compared to other strategies, such as Database Per Tenant or Schema Per Tenant.
One advantage is that each tenant can have a unique data shape, meaning they can define their own tables, views, and other objects without affecting other tenants. This allows for more flexibility and customization for each tenant. Therefore, option D is correct.
Another advantage is that storage costs can be optimized, because each tenant can use their own storage credits and manage their own data retention policies. This also reduces the risk of data spillover or cross-tenant access. Therefore, option E is correct.
However, this approach also has some drawbacks, such as:
It requires more administrative overhead and complexity to manage multiple accounts and their resources.
It may not optimize compute costs, because each tenant has to provision their own warehouses and pay for their own compute credits. This may result in underutilization or overprovisioning of compute resources. Therefore, option C is incorrect.
It may not simplify security and RBAC policies, because each account has to define its own roles, users, and privileges. This may increase the risk of human errors or inconsistencies in security configurations. Therefore, option B is incorrect.
It may not reduce the number of objects per tenant, because each tenant still has to create their own databases, schemas, and other objects within their account. This may affect the performance and scalability of the application. Therefore, option A is incorrect.
References: : Multi-Tenant Application Strategies

NEW QUESTION # 75
A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?

• A. An external table
• B. A pipe
• C. A copy command at regular intervals
• D. A stream

Answer: B

Explanation:
A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.
References: : Pipes : Loading Data Using Snowpipe : External Tables : Streams : COPY INTO <table>

NEW QUESTION # 76
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously ang efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

• A. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• B. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• C. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector.
  Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
• D. Ingest the data using COPY INTO and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Answer: A

Explanation:
This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution.
They are also efficient, scalable, and serverless, and they simplify the data transformation process. External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.
References:
Snowpipe Overview | Snowflake Documentation
Introduction to Data Pipelines | Snowflake Documentation
External Functions Overview | Snowflake Documentation
Snowflake Data Marketplace Overview | Snowflake Documentation

NEW QUESTION # 77
......

ARA-R01 Exam Dumps Pass with Updated 2024 Certified Exam Questions: https://dumpspdf.free4torrent.com/ARA-R01-valid-dumps-torrent.html